A Prognostic Cuproptosis-Related LncRNA Signature for Colon Adenocarcinoma

Background Cuproptosis, a recently discovered form of cell death, is caused by copper levels exceeding homeostasis thresholds. Although Cu has a potential role in colon adenocarcinoma (COAD), its role in the development of COAD remains unclear. Methods In this study, 426 patients with COAD were extracted from the Cancer Genome Atlas (TCGA) database. The Pearson correlation algorithm was used to identify cuproptosis-related lncRNAs. Using the univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) was used to select cuproptosis-related lncRNAs associated with COAD overall survival (OS). A risk model was established based on the multivariate Cox regression analysis. A nomogram model was used to evaluate the prognostic signature based on the risk model. Finally, mutational burden and sensitivity analyses of chemotherapy drugs were performed for COAD patients in the low- and high-risk groups. Result Ten cuproptosis-related lncRNAs were identified and a novel risk model was constructed. A signature based on ten cuproptosis-related lncRNAs was an independent prognostic predictor for COAD. Mutational burden analysis suggested that patients with high-risk scores had higher mutation frequency and shorter survival. Conclusion Constructing a risk model based on the ten cuproptosis-related lncRNAs could accurately predict the prognosis of COAD patients, providing a fresh perspective for future research on COAD.


Introduction
Colonic adenocarcinoma (COAD) is the most common histological subtype of colorectal cancer and is one of the leading causes of cancer mortality [1]. With the development of substantive treatment strategies, including surgery, neoadjuvant therapy, and targeted therapy, the overall prognosis for patients with COAD has signifcantly improved [2]. At the same time, the importance of early diagnosis of COAD for prognosis is being increasingly recognized. Te 5-year survival rate of patients with early diagnosis is approximately 90%, but only 10% for patients diagnosed with advanced metastatic disease [1]. Identifying novel biomarkers for tumor diagnosis and prognosis has been shown to beneft the treatment of diverse tumor types [3][4][5][6]. Terefore, there is still an urgent need to identify novel prognostic biomarkers associated with metastasis to facilitate the timely diagnosis and earlier application of appropriate, individualized therapy.
Long noncoding RNAs (lncRNAs) are transcripts over 200 nucleotides in length with no signifcant protein-coding function [7]. By modulating gene expression, lncRNAs have been reported to play important roles in many physiological processes and disease progression [8]. In COAD, a variety of lncRNAs have been reported to be highly expressed and have been associated with multiple tumor-related biological processes, including proliferation, chemical resistance, and epithelial-mesenchymal transformation [9][10][11][12]. Tese lncRNAs have been associated with the activation of multiple signaling pathways, including WNT, PI3K/Akt, and PPAR [13]. Considering the roles of these pathways in the occurrence and development of COAD [14,15], lncRNAs are likely to be signifcant factors in tailoring individualized therapies. Several studies have identifed lncRNAs as potential therapeutic targets [16][17][18]. Overexpression of LINC00152 has been shown to promote the expression of fascin actin-binding protein 1 (FSCN1) by binding mir-632 and mir-185-3p, leading to proliferation and metastasis [19]. As reviewed in 2022, lncRNAs including DCST1-AS1, LINC01569, KCNQ1OT1, and LINC00997 were considered to take an active part in carcinogenesis by infuencing cell metastasis, drug resistance, radio-resistance, and tumor microenvironment interaction [20]. However, the role of lncRNAs in COAD has not been completely elucidated.
Cu levels are elevated in the serum and tissues of multiple solid tumors, including colorectal tumors [21]. However, its role is not fully understood. On one hand, in addition to acting as a cofactor for key metabolic enzymes, Cu also directly promotes tumor growth by acting as a cofactor for signaling molecules such as MEK1, which transduces carcinogenic BRAF signals to ERK1/2 [22], suggesting that it may have a key role in cancer progression. On the other hand, the ion carrier elesclomol mediates copper overload in colorectal cancer cells and induces copper-dependent cell death by degrading ATP7A [23].
Tis cell death pathway, caused by copper levels exceeding homeostasis thresholds, is called copper death or cuproptosis [24]. It relies on mitochondrial respiration [25]. Copper binds directly to the lipid components of the tricarboxylic acid (TCA) cycle, resulting in the accumulation of lipoacylated proteins, followed by the loss of iron-sulfur cluster proteins, resulting in proteotoxic stress and cell death [26]. Cuproptosis caused by copper overload has been shown to predict tumor prognosis and judge immune and drug responses in a variety of tumors, including head and neck squamous cell carcinoma, breast cancer, and cervical cancer [27][28][29][30]. However, there is no relevant report found in COAD. Terefore, the double-edged role of copper in colorectal cancer and its infuence on prognosis need to be further analyzed and understood.
In this study, we examined cuproptosis-associated lncRNAs in the clinical context of COAD using the Cancer Genome Atlas (TCGA) database. We constructed a risk model to evaluate the prognostic ability of cuproptosisassociated lncRNAs in patients with COAD. Te tumor mutational burden and sensitivity analysis of chemotherapy drugs were also assessed. Taken together, our fndings provide new insights into potential therapeutic strategies for patients with COAD.

Data Collection.
Gene expression matrices and clinical information for patients with COAD were obtained from the Cancer Genome Atlas database (https://portal.gdc.cancer. gov/). We identifed 426 such samples for inclusion. Te gene expression matrices were merged using a Perl script for further analysis. Genes encoding lncRNAs and mRNAs were annotated and classifed using the Human Genome Browser, GRCh38.p13 (https://asia.ensembl.org/index.html). Survival time, survival status, age, sex, stage, and TNM stage were extracted from the TCGA database using Perl scripts. All data and clinical information used in this study were obtained from a public database; therefore, neither approval from the ethics committee nor written informed consent from patients was required.

Prognostic Signature Construction. Based on univariate
Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) algorithm was performed using the R package "glmnet." Te multivariate Cox regression analysis was used to evaluate the lncRNA signature as an independent prognostic factor for patient survival. Risk scores for each patient were calculated using the following formula: risk scores � n i�1 Coef(i) × x(i), where Coef(i) represents the correlation regression coefcient and x(i) is the expression level of cuproptosis-related lncRNAs. Patients with COAD were divided into low-and high-risk groups based on median risk scores. Kaplan-Meier survival analysis was employed to assess the diference in OS rates in the low-and high-risk groups using the log-rank algorithm. A 3D principal component analysis (3D-PCA) was conducted to assess the diference in signatures between lowand high-risk patients using the R package "ggplot2."

Consensus Clustering Analysis.
According to the prognostic cuproptosis-related genes, consensus clustering was performed using the R package "ConsensusClusterPlus." Te clustering was established on the grounds of partitioning around medoids with "Euclidean" distances, and 1,000 verifcations were performed. Finally, with the optimal classifcation of K � 2-9, the patients with COAD were clustered into two subtypes for further analysis.

Risk Model Independence.
Te univariate and multivariate Cox regression analyses were used to assess risk scores as independent prognostic factors for COAD. A subtype analysis was conducted to confrm the independence of the risk model. To further determine whether the risk score was independent of other clinical variables, including age, Gleason score, PSA value, and T stage, patients were regrouped into new subtypes based on diferent clinical characteristics. According to median risk scores, patients in each subtype were stratifed into low-and high-risk groups.

Drug Sensitivity Analysis.
Based on the Genomics of Drug Sensitivity Genomics in Cancer (GDSC), the drug treatment response of each patient with COAD was predicted using the R package "pRRophetic." Diferences in IC 50 values between low-and high-risk groups were analyzed using the "ggplot2" R package.

Gene Set Enrichment Analysis (GSEA).
For the low-and high-risk groups, 1,000 permutations were used and screened using the largest and smallest gene set flters of 500 and 15 genes, respectively. P values less than 0.05 were considered to be signifcantly diferent.
2.9. Statistical Analysis. All analyses were performed using the R software (version 3.6.0) and Perl scripts. Te Wilcoxon rank sum test was applied to separately conduct group comparisons with P values less than 0.05, which was considered to be statistically signifcant.

Identifcation of Cuproptosis-Related lncRNAs.
A total of 14,142 lncRNAs were collected from the TGCA COAD RNA-Seq matrix. To identify lncRNAs related to cuproptosis, correlations between the expression of cuproptosis genes and lncRNAs were calculated, yielding a total of 870 candidate lncRNAs. Using the univariate Cox regression analysis, 15 cuproptosis-related lncRNAs associated with OS were selected using the least absolute shrinkage and selection operator (LASSO) algorithm ( Figure 1, Supplementary Using the median risk score, the COAD patients were divided into the following two groups: 213 patients in the low-risk group and 213 patients in the high-risk group. Patients were ranked according to the cuproptosis-related prognostic signature; the resulting scatter dot plot indicated that survival time was inversely correlated with risk score (Figures 2(a) and 2(b)). Te Kaplan-Meier survival analysis showed that the OS of patients with high-risk scores was signifcantly shorter than that of those with low-risk scores (P � 1.553E − 08, Figure 2(c)). A 3D principal component analysis (3D-PCA) produced a clear separation between low-and high-risk groups based on the selected lncRNAs ( Figure 2(d)). Of the ten prognostic cuproptosis-related lncRNAs, AL161729.4, AC068580.3, AL138756.1, MIR210HG, EIF3J-DT, LINC02381, AC010973.2, ZEB1-AS1, and AC073957.3 were expressed at higher levels in the high-risk group, whereas TNFRSF10A-AS1 was expressed at higher levels in the low-risk group (Figure 2(e)). Tese results suggested that constructing a risk model based on the ten cuproptosisrelated lncRNAs is prognostic for patients with COAD.

Training and Validation
Cohorts. Te COAD patients were randomly classifed into training and validation cohorts. In both cohorts, patients were ranked by median risk score. A scatter dot plot showed that survival times of COAD patients in the training and validation cohorts were conversely associated with risk scores (Figures 3(a) and 3(b)). Te survival of patients with low-risk scores was higher than that of patients with high-risk scores in both cohorts (P < 0.001, Figures 3(c) and 3(d)). Tese results demonstrated that our risk model is accurate and reliable.

Correlations between lncRNA Risk Scores and Clinico
, and <65 (P � 2.61e − 03). However, the survival rate was similar between T-stage groups ( Figure 5). Tese results indicate that the prognostic signature based on cuproptosis-related lncRNAs accurately predicts prognosis relative to clinicopathological characteristics.

Consensus
Clustering Analysis for Cuproptosis-Related lncRNAs associated with COAD. Tereafter, consensus clustering analysis was utilized to cluster the patients with COAD into diferent subgroups, and the result revealed an optimal classifcation for consensus clustering with K � 2 (Figures 6(a)-6(c)). Based on the prognostic cuproptosisrelated lncRNAs, the patients with COAD were successfully Journal of Oncology divided into two subgroups, with 323 patients in Cluster A and 103 patients in Cluster B. Te principal component analysis result illustrated a clear separation between Cluster A and Cluster B according to the prognostic cuproptosisrelated lncRNAs (Figure 6(d)). Te Kaplan-Meier survival curve analysis suggested that the patients in Cluster A had a higher OS rate than those in Cluster B (Figure 6(e)). Tese results demonstrate that the cuproptosis-related lncRNAs are associated with the prognosis of COAD.

Nomogram Construction.
A nomogram was constructed to confrm the accuracy of the prognostic signature and clinicopathological characteristics (Figure 7(a)). It yielded a consistency index (C-index) of 0.727. Calibration curves indicated that the nomogram-predicted 1, 3, and 5-year survival rates were consistent with actual survival times ( Figure 7(b)). Time-dependent ROC curves revealed that the AUCs of 1-, 3-, and 5-year were 0.704, 0.731, and 0.775, respectively, indicating satisfactory accuracy of the model (Figure 7(c)).

Tumor Mutational Burden (TMB) Analysis. TMB indices
for high-risk and low-risk genes were calculated. As shown in Figure 8(a), patients with high TMB had lower survival rates than those with low TMB (P � 0.025). Te mutation frequencies of high-risk genes were higher than those of low-risk genes. Survival of the high-TMB + high-risk panel was the lowest, followed by the low-TMB + high-risk, high-TMB + low-risk, and low-TMB + low-risk panels (Figure 8            Journal of Oncology 11 TTN (46%), and KRAS (47%) had the highest mutation frequencies. In the high-risk group, mutations were detected in 185 out of 196 samples. Te mutated genes with the highest frequency in the mutation map showed no signifcant diference compared with the previous group (Figure 8(d)).
3.9. Sensitivity to Chemotherapeutic Agents. As chemotherapy is the primary treatment for newly diagnosed COAD, we compared IC 50 values for several commonly used drugs between the low-and high-risk groups. IC 50 values for highrisk COAD patients for nilotinib, rapamycin, geftinib, salubrinal, GSK.650394, shikonin, lenalidomide, tipifarnib, and vinblastine were all lower (P < 0.05), while the IC 50 for bicalutamide was higher in the high-risk group (Figure 9). Tese results provide preliminary evidence for clinical druguse guidance.

Gene Set Enrichment Analysis (GSEA).
We found multiple KEGG signaling pathways that were dynamically enriched in the low-risk group compared to the high-risk group, including those involved in the citrate cycle of the TCA cycle; propanoate metabolism, arginine, and proline metabolism; alanine, aspartate, and glutamate metabolism; proteasome; and valine, leucine, and isoleucine degradation. Notably, the expression of components of the mTOR signaling pathway was signifcantly increased in the high-risk

Conclusions
Despite signifcant improvements in surgery, radiotherapy, chemotherapy, and immunotherapy, the 5-year COAD survival rate remains very low [1]. Terefore, it is important to identify potential biomarkers for diagnosis and treatment. In this study, we identifed and validated a ten-gene feature that predicted survival in patients with COAD. Tis risk model may be clinically valuable for identifying patients for individualized, cuproptosis-inducing therapy.
Gene expression is regulated by the interaction of lncRNAs with RNA, DNA, and proteins through a variety of mechanisms, including regulation of transcription, mRNA stability, and translation [31]. In colon cancer, lncRNAs have been implicated in regulating cell proliferation, apoptosis, the cell cycle, cell migration and invasiveness, epithelialmesenchymal transformation (EMT), cancer stem cells, and drug resistance [32]. Multiple types of lncRNAs have been correlated with COAD prognosis [33]. Copper-based therapies are considered to have great potential in cancer treatment; some are already in clinical trials. However, their anticancer potential has not been fully elucidated [34]. Cuproptosis is a newly discovered form of cell death that involves mitochondrial metabolic activity and has not been thoroughly studied in tumors [26]. In the current study, ten lncRNAs associated with cuproptosis were identifed and included in a risk model. Te Kaplan-Meier curve, timedependent ROC curve, and Cox regression analysis all demonstrated the predictive ability of the risk model, indicating an independent predictor of COAD prognosis.
Progressive preclinical and clinical evidence suggests that targeting mitochondrial metabolism has anticancer efects [35,36]. Cuproptosis is associated with highly reactive mitochondrial oxidative phosphorylation (OXPHOS) [26]. Despite an increasing reliance on glycolysis, cells from many cancer types still exhibit functional OXPHOS [37]. In colon adenocarcinomas, stem cells have been reported to use mitochondrial OXPHOS to produce ATP and maintain mitochondrial function via the FOXM1/PRDX3 pathway, thereby maintaining their survival and stem-cell characteristics [38].
Among the lncRNAs screened, MIR210HG, EIF3J-DT, and ZEB1-AS1 have been extensively studied in tumors. MIR210HG promotes breast cancer progression through m6A modifcation mediated by IGF2BP1 [39]. IGF2BP1 also plays an important role in COAD pathogenesis. Its deletion downregulates k-RAS expression downstream of β-catenin and simultaneously inhibits colon cancer cell proliferation, whereas IGF2BP1 overexpression increases c-MYC and K-RAS expression and promotes colon cancer cell proliferation [40]. Whether MIR210HG is involved in this pathway in COAD requires further investigation. In gastric cancer, EIF3J-DT is involved in the regulation of autophagy and chemical resistance of gastric cancer cells by targeting ATG14 [41], while autophagy-dependent apoptosis has been shown to be a promising therapeutic target in COAD [42]. ZEB1-AS1 is involved in the regulation of the ZEB1 pathway; its activation has been reported to promote the stem characteristics and invasiveness of COAD cells [32,43]. Te aforementioned evidence suggests functional mechanisms by which the lncRNAs we identifed may be involved in COAD and suggests ways for improving chemotherapy sensitivity and prognosis. Considering our insufcient understanding of these lncRNAs, further studies on them are of clear clinical value. We found decreased sensitivity to multiple chemotherapeutic agents in the high-risk group stratifed by CPR-related prognosis. Te development of chemoresistance is an important factor that limits the therapeutic efcacy of anticancer drugs and ultimately leads to the failure of COAD chemotherapy [44]. Transport-based mechanisms of cellular drug resistance play important roles [45,46]. Trough the control of entry and exit of substrates through the cell membrane by membrane transporters, such as P-gp, multiple drugs can escape from cancer cells, decreasing their intracellular accumulation, resulting in multidrug resistance (MDR) that is not limited to a specifc type and confers resistance to multiple drugs [47]. Studies on MDR mechanisms and strategies for their reversal play an important role in the success of chemotherapy [48][49][50]. Tere have been studies showing that a new class of thiosemicarbazone compounds, the copperbinding di-2-pyridyl ketone thiosemicarbazones, has great promise. Trough a unique mechanism, they form redoxactive complexes with copper in the lysosomes of cancer cells to reduce the amount of copper in the body, thereby overcoming P-gp-mediated MDR [51]. Terefore, chelators that bind copper have been developed as anticancer agents [51]. Our data on decreased sensitivity to multiple chemotherapeutic agents in patients with COAD in the lncRNAstratifedhigh-risk group may also be due to higher Cu concentrations. Te targeted application of chelators that bind copper to fght cancer progression and chemoresistance has signifcant clinical potential.
In conclusion, we identifed ten cuproptosis-related lncRNAs using the multivariate Cox regression analysis and constructed a risk model that can accurately predict COAD prognosis. Tis evidence provides a foundation for future research on COAD. Our study had some limitations. All analyses were performed using a TCGA-COAD cohort and have not been validated against other databases. Additionally, in vivo and in vitro experiments should be performed for further validation. Further exploration of the impact of cuproptosis on prognosis and chemotherapy resistance in COAD may provide new ideas for further study and clinical applications.

Data Availability
All data and clinical information involved in this study were obtained from a public database (https://portal.gdc.cancer. gov/) approved by the ethics committee.

Consent
Written informed consent from patients was not required.

Conflicts of Interest
Te authors declare that there are no conficts of interest regarding the publication of this paper.